depictr - A Unified Toolkit for Visualising Statistical Models and Data
A cohesive, publication-ready toolkit of plots that span
the whole analysis workflow with one consistent look. It covers
exploratory data analysis (distributions, categorical
summaries, bivariate plots, scatter-plot matrices, correlation
heatmaps, missing-data maps, outliers, estimation statistics
and descriptive tables); multivariate analysis, clustering with
diagnostics and Kaplan-Meier survival curves; time series
(trends, autocorrelation, decomposition, seasonality and
forecasting); model estimates and inference (forest plots,
model comparison, frequentist and Bayesian estimates, predicted
values, interactions, random effects and optimiser checks);
diagnostics and classification (residual panels, binned
residuals, influence, quantile-quantile, receiver operating
characteristic (ROC) curves, calibration, threshold tuning and
confusion matrices); uncertainty and power; and reporting
helpers (a shared theme, colourblind-aware palettes, plot
composition and saving). Every plotting function returns a
'ggplot2' object (or a 'patchwork' object for composite
panels), heavier modelling back-ends are optional, and the
package ships with reproducibly simulated datasets so that
every example and vignette runs without further setup.
